Abstract

The problem of predicting the 3D native conformation of a polypeptide given the primary sequence is of fundamental importance in contemporary biology. Here we present a novel Genetic algorithm method to obtain the optimal geometry of a pentapeptide. The pentapeptide moiety is found in wide set of configurations in protein databases. A genetic algorithm utilizes optimization procedure similar to natural genetic evolution. Unlike other genetic algorithms, which are constrained to operate on bit strings, the algorithm used here operates on real values. A random population of conformations is generated taking into account the constraints of Ramachandran plot. This population advances through successive generations in which the solutions evolve via genetic operators (Mutate, Variate and Crossover); in such a way that it gives both better survival chance to fitter soluctions and a larger diversity within the population. More attention will be given to favorable local structures while unfavorable local structures will be rapidly abandoned on the basis of their fitness functions (potential energy), which are calculated using the GROMOS-96 force field of Spdbv version-3.7. The SCWRL3.0 program developed at Dunbrack lab is used for placing side chains to a fixed backbone coordinates. This algorithm proved to be very efficient for the penta-peptide Met-Enkephalin, a natural Opioid polypeptide produced in the brain. The algorithm converged only in 41 minutes and in 23 generations keeping the population size fixed (20 individual per population). The converged structure is energetically as stable as the native conformation of the protein. Its shows a RMSD of 5.9 from the original native conformation.